Data compression techniques aim to optimize storage and transmission of data by removing redundant information. Lossless compression methods like Run-Length Encoding and Huffman Coding replace repetitive patterns with codes to reduce file size without losing any original data. Lossy methods like JPEG, MPEG, and MP3 are commonly used for images, video, and audio as they allow for smaller file sizes by discarding imperceptible data. These methods break files into blocks, apply transforms to reveal redundancies, then quantize and encode the data.

1. Data Compression

2. Why Data Compression?
 Make optimal use of limited storage space
 Save time and help to optimize resources
 If compression and decompression are done in I/O processor, less time is required to
move data to or from storage subsystem, freeing I/O bus for other work
 In sending data over communication line: less time to transmit and less storage to host

3. Data Compression- Entropy
 Entropy is the measure of information content in a
message.
 Messages with higher entropy carry more information than messages
with lower entropy.
 How to determine the entropy
 Find the probability p(x) of symbol x in the message
 The entropy H(x) of the symbol x is:
H(x) = - p(x) • log2p(x)
 The average entropy over the entire message is the
sum of the entropy of all n symbols in the message

4. Data Compression Methods
 Data compression is about storing and sending a
smaller number of bits.
 There’re two major categories for methods to
compress data: lossless and lossy methods

5. Lossless Compression Methods
 In lossless methods, original data and the data after
compression and decompression are exactly the
same.
 Redundant data is removed in compression and
added during decompression.
 Lossless methods are used when we can’t afford to
lose any data: legal and medical documents, computer
programs.

6. Run-length encoding
 Simplest method of compression.
 How: replace consecutive repeating occurrences of a symbol by 1
occurrence of the symbol itself, then followed by the number of
occurrences.
 The method can be more efficient if the data uses only 2 symbols (0s and
1s) in bit patterns and 1 symbol is more frequent than another.

7. Huffman Coding
 Assign fewer bits to symbols that occur more frequently and
more bits to symbols appear less often.
 There’s no unique Huffman code and every Huffman code has
the same average code length.
 Algorithm:
a. Make a leaf node for each code symbol
Add the generation probability of each symbol to the leaf node
a. Take the two leaf nodes with the smallest probability and connect them into
a new node
Add 1 or 0 to each of the two branches
The probability of the new node is the sum of the probabilities of the
two connecting nodes
a. If there is only one node left, the code construction is completed. If not, go
back to (2)

8. Huffman Coding
 Example

9. Huffman Coding
 Encoding
 Decoding

10. Lempel Ziv Encoding
 It is dictionary-based encoding
 Basic idea:
 Create a dictionary(a table) of strings used during communication.
 If both sender and receiver have a copy of the dictionary, then
previously-encountered strings can be substituted by their index in
the dictionary.

11. Lempel Ziv Compression
Have 2 phases:
 Building an indexed dictionary
 Compressing a string of symbols
• Algorithm:
 Extract the smallest substring that cannot be found in the remaining
uncompressed string.
 Store that substring in the dictionary as a new entry and assign it an
index value
 Substring is replaced with the index found in the dictionary
 Insert the index and the last character of the substring into the
compressed string

12. Lempel Ziv Compression
 Compression
example:

13. Audio Encoding
 Predictive encoding
 Only the differences

14. Lempel Ziv Decompression
 It’s just the inverse
of compression process

15. Lossy Compression Methods
 Used for compressing images and video files (our
eyes cannot distinguish subtle changes, so lossy data
is acceptable).
 These methods are cheaper, less time and space.
 Several methods:
 JPEG: compress pictures and graphics
 MPEG: compress video
 MP3: compress audio

16. JPEG Encoding
 Used to compress pictures and graphics.
 In JPEG, a grayscale picture is divided into 8x8
pixel blocks to decrease the number of
calculations.
 Basic idea:
 Change the picture into a linear (vector) sets of numbers that
reveals the redundancies.
 The redundancies is then removed by one of lossless
compression methods.

17. JPEG Encoding- DCT
 DCT: Discrete Concise Transform
 DCT transforms the 64 values in 8x8 pixel block in a way that
the relative relationships between pixels are kept but the
redundancies are revealed.
 Example:
A gradient grayscale

18. Quantization & Compression
Quantization:
 After T table is created, the values are quantized to reduce
the number of bits needed for encoding.
 Quantization divides the number of bits by a constant, then
drops the fraction. This is done to optimize the number of
bits and the number of 0s for each particular application.
• Compression:
 Quantized values are read from the table and redundant 0s
are removed.
 To cluster the 0s together, the table is read diagonally in an
zigzag fashion. The reason is if the table doesn’t have fine
changes, the bottom right corner of the table is all 0s.
 JPEG usually uses lossless run-length encoding at the
compression phase.

19. JPEG Encoding

20. MPEG Encoding
 Used to compress video.
 Basic idea:
 Each video is a rapid sequence of a set of frames. Each frame is a
spatial combination of pixels, or a picture.
 Compressing video =
spatially compressing each frame
+
temporally compressing a set of frames.

21. MPEG Encoding
 Spatial Compression
 Each frame is spatially compressed by JPEG.
• Temporal Compression
 Redundant frames are removed.
 For example, in a static scene in which someone is talking, most
frames are the same except for the segment around the speaker’s
lips, which changes from one frame to the next.

22. Audio Compression
 Used for speech or music
 Speech: compress a 64 kHz digitized signal
 Music: compress a 1.411 MHz signal
• Two categories of techniques:
 Predictive encoding
 Perceptual encoding

23. Audio Encoding
Predictive Encoding
 Only the differences between samples are encoded, not the whole
sample values.
 Several standards: GSM (13 kbps), G.729 (8 kbps), and G.723.3 (6.4
or 5.3 kbps)
• Perceptual Encoding: MP3
 CD-quality audio needs at least 1.411 Mbps and cannot be sent over
the Internet without compression.
 MP3 (MPEG audio layer 3) uses perceptual encoding technique to
compress audio.

24. Thank-You